NeuroImage 46 (2009) 257–269

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Cognitive function and brain structure correlations in healthy elderly East Asians

Michael W.L. Chee a,⁎, Karren H.M. Chen a, Hui Zheng a, Karen P.L. Chan a, Vivian Isaac a, Sam K.Y. Sim a,Lisa Y.M. Chuah a, Maria Schuchinsky a, Bruce Fischl b, Tze Pin Ng c

a Cognitive Neuroscience Laboratory, Duke-NUS Graduate Medical School Singapore, 7 Hospital Drive, Block B, #01-11, Singapore 169611, Singaporeb Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, USAc Gerontological Research Programme, Faculty of Medicine, National University of Singapore, Singapore

⁎ Corresponding author. Fax: +65 62246386.E-mail address: michael.chee@duke-nus.edu.sg (M.W

1053-8119/$ – see front matter © 2009 Elsevier Inc. Aldoi:10.1016/j.neuroimage.2009.01.036

a b s t r a c t

a r t i c l e i n f o

Article history:

We investigated the effect Received 16 October 2008Revised 15 December 2008Accepted 22 January 2009Available online 3 February 2009

Keywords:Cognitive agingCohort studiesMRIVolumetryCortical thicknessWhite matter

of age and health variables known to modulate cognitive aging on severalmeasures of cognitive performance and brain volume in a cohort of healthy, non-demented persons ofChinese descent aged between 55 and 86 years. 248 subjects contributed combined neuropsychological, MRimaging, health and socio-demographic information. Speed of processing showed the largest age-relateddecline. Education and plasma homocysteine levels modulated age-related decline in cognitive performance.Total cerebral volume declined at an annual rate of 0.4%/yr. Gray and white matter volume loss wascomparable in magnitude. Regionally, there was relatively greater volume loss in the lateral prefrontal cortexbilaterally, around the primary visual cortex as well as bilateral superior parietal cortices. Speed of processingshowed significant positive correlation with gray matter volume in several frontal, parietal and midlineoccipital regions bilaterally. In spite of differences in diet, lifestyle and culture, these findings are broadlycomparable to studies conducted in Caucasian populations and suggest generalizability of processes involvedin age-related decline in cognition and brain volume.

© 2009 Elsevier Inc. All rights reserved.

Introduction

Maintaining optimal cognitive function for as long as possible isa vital element of successful aging (Rowe and Kahn, 1987) and thisgoal has motivated many cognitive and imaging studies of brainaging. With possibly one exception (Mu et al., 1999) all such studieshave been conducted in predominantly Caucasian populations(Carlson et al., 2008; Fotenos et al., 2005; Prins et al., 2002; Razet al., 1998; Resnick et al., 2003; Scahill et al., 2003).

As the additional resources needed to care for disabled elderlycould significantly compound the pressure exerted on global energyand food availability, there is an urgent need for accurate informationabout brain and cognitive aging among Asians — who constitute themost rapidly aging population grouping in the world. To illustrate,in 1982, adults over the age of 65 years represented only 4.9% ofthe Chinese population (Liang et al., 1985). This increased to 6.96%of 1.3 billion in 2000 (National Bureau of Statistics People'sRepublic of China, 2001), and could rise to 23.7% of 1.4 billion in2050 (Population division of the department of economic andsocial affairs of the United Nations Secretariat, 2007) i.e. equivalentto the entire United States population in 2006.

The rate of cognitive decline and brain atrophy can be influenced byeducation (Staff et al., 2004) as well as a variety of cardiovascular risk/

.L. Chee).

l rights reserved.

fitness factors (Colcombe et al., 2004; Murray et al., 2005; Raz et al.,2003a) inways that probably generalize across populations. However,diet (Kalmijn et al., 2004; Mattson, 2003), environmental factors andgenetic makeup differ across ethnic groups and could affect the agingprocess (Bamshad, 2005; Kirkwood, 2005). Additionally, culture hasbeen shown to influence cognition (Nisbett andMiyamoto, 2005; Parkand Gutchess, 2002) and modulate task-related brain activation (Gohet al., 2007).

Comparing rates of change of brain volume across aging studiesrequires attention to differences in image acquisition and qualitycontrol (Littmann et al., 2006; Preboske et al., 2006), sample sizeand age span of the cohort (Fotenos et al., 2005; Jernigan andGamst, 2005), health of volunteers (Resnick et al., 2003), imagemeasurement technique (Gunter et al., 2003) and method ofcorrection for differences in head size (Buckner et al., 2004). Therange in findings across studies makes it difficult to assess what is‘normal’ for a particular group or to judge the benefit ofenvironmental modifiers or the efficacy of interventions that couldreduce the impact of age-related change in cognition. Thesechallenges are compounded by the fact that excellent imagingdata may not be accompanied by detailed neuropsychologicaltesting or associated health information and vice versa. Suchpractical realities have motivated the formation of multi-laboratoryconsortiums to standardize data collection (Jack et al., 2008;Mueller et al., 2005), so as to afford the establishment of baselinedata that has robust clinical utility.

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In recognition of the methodological issues that could confoundthe interpretation of contemporary structural imaging studies, wereport cross-sectional data originating from a large (248 subjects),single-centre, longitudinal aging study that incorporated recentadvancements in image acquisition, quality control (Mallozzi et al.,2006, 2004), image processing (Jovicich et al., 2006) and analysis(Desikan et al., 2006; Fischl et al., 2004) techniques.

We focused on a ‘post-retirement’ age range of 55–86 years,instead of attempting to characterize lifespan changes, because age-related changes in cognition have the greatest economic and societalimpact in this segment of the population. In addition to age, weevaluated other factors that could affect cognitive performance(Enzinger et al., 2005). We obtained a number of measures of brainstructure and evaluated the effect of age and health variables ofinterest on these measures. Mindful that pre-existing chronic illnesscan influence imaging findings (Resnick et al., 2003) we prospectivelyselected volunteers who met strict health criteria in order that ourresults would represent a standard an average middle class East Asianindividual could benchmark against. Finally, we correlated measuresof cognitive performance and brain structure. In view of the increasingautomation in brain structure measurement, we made head-to-headcomparisons between manual and semi-automated measurements ofthree commonly reported brain measures as a preface to moreextensive data analysis using this methodology. This cross sectionaldata, while primarily descriptive and comparative in nature, shouldprovide a valuable starting point from which to base future studiesconcerning brain aging in Asians.

Methods

Participants

The volunteers weremembers of the Singapore Longitudinal AgingBrain Study, a community-based epidemiologic study involvinghealthy elderly volunteers that sought to characterize age-relatedbrain changes and cognitive performance in persons of Chinesedescent resident in Singapore. The study was approved by theSingapore General Hospital Institutional Review Board and partici-pants gave informed consent prior to undergoing evaluation.

349 healthy adults participated in the first wave of the study; datafrom 248 volunteers are reported here. 285 of the participants wererecruited through newspaper advertisements and from active agingclubs. The remaining 64 volunteers were participants from a separatecommunity-based longitudinal aging study who agreed to undergoneuroimaging. All participantswere screened in a telephone interviewbefore undergoing a structured interview at the laboratory.

Participants were persons aged 55 years and above with no knownactive medical conditions other than treated, uncomplicated diabetesmellitus or hypertension. Participants were excluded if they had any ofthe following: (1) history of significant vascular events (i.e.,myocardial infarction, stroke or peripheral vascular disease); (2)history of malignant neoplasia of any form; (3) a history of cardiac,lung, liver, or kidney failure; (4) active or inadequately treated thyroiddisease; (5) active neurological or psychiatric conditions; (6) a historyof head trauma with loss of consciousness; (7) a Mini-Mental StateExamination (MMSE) (Folstein et al., 1975) score b26; (8) a 15-pointmodified-Geriatric Depression Screening Scale (GDS) (Sheikh andYesavage, 1986) score N9; or (9) a history of illicit substance use.Participants could be excluded on the basis of disqualifying informa-tion obtained during the structured interview, results of blood tests, orself-reports of medication and supplement intake.

Blood tests

Venous blood samples were drawn between 8:30 am and 9:30 amafter an overnight fast and tested for the following: APOE genotype,

total fasting glucose level, total fasting cholesterol, HDL-cholesterol,LDL-cholesterol, triglycerides, cholesterol/HDL ratio, C-reactive pro-tein, homocysteine, folate, and vitamin B12. Measurements weremade at the National University Hospital Laboratory using quality-controlled procedures.

Neuropsychological assessment

Participants were assessed between 10 am and 2 pm, within3 months of undergoing MR imaging by trained researchers whoworked under the supervision of a clinical neuropsychologist. Abattery of 11 neuropsychological tests evaluating six cognitivedomains— attention, verbal memory, visuospatial memory, executivefunctioning, speed of processing, and language was used. Weminimized the effects of language and culture by using tests thatcontained items that were relatively familiar to the study population.Attention was assessed using the Digit Span subtest from theWechsler Memory Scale III (Wechsler, 1997) and a computerizedversion of a Spatial Span task. Verbal memory was evaluated usingthe Rey Auditory Verbal Learning Test (RAVLT) (Lezak et al., 2004)and a Verbal-Paired Associates test. Visuospatial memory wasevaluated using the Visual Reproduction (VR) subtest from theWMS-III and a Visual Paired Associates test. Executive functioningwas assessed using a Categorical Verbal Fluency test (usingcategories of animals, vegetables and fruits), the Design Fluencytest (Delis et al., 2001), and the Trail Making Test B (Reitan andWolfson, 1985). Speed of processing was assessed with the Trail-Making Test A (Reitan and Wolfson, 1985) and the Symbol-DigitModalities Test (SDMT) (Smith, 1991). Language was evaluated usingthe Object and Action Naming Battery (Druks and Masterson, 2000).The tests were administered in either English or Mandarin accordingto the subject's most proficient language. The individual test scoreswere standardized (z-transformed) and combined into six theoreti-cally motivated composite scores (attention, verbal memory, visuos-patial memory, speed of processing, executive functions andlanguage) to limit the number of comparisons.

MR imaging

MRI was performed on a 3T Siemens Allegra (Siemens, Erlangen,Germany) system using a standardized imaging procedure that incor-porated a number of quality control measures. Each day, followingthermal stabilization of the MR system, a 165-sphere phantom (ThePhantom Laboratory, Salem, NY) was scanned to evaluate geometricdistortion and signal to noise ratio (Mallozzi et al., 2006). The samegradient system and 4-channel head coil were used throughout thestudy.

Participants were carefully positioned in the magnet to lie withinthe centre of the spherical 22 cm ‘sweet spot’ of the head coil.Participants whose necks were short or whose heads were too large tofulfill this requirement were excluded from the data analyses.Participants were instructed to have their usual amount of liquidprior to scanning in order tominimize the effect of hydration status onbrain volume (Duning et al., 2005). To minimize the requirement for apost-hoc image reorientation, we acquired high-resolution sagittalT1-weighted images keeping the long axis of the left hippocampusparallel to the imaging volume.

The T1-weighted MP-RAGE sequence used for morphometricanalysis provided excellent gray-white matter contrast as well asgray matter-cerebrospinal fluid contrast. It was identical to that usedby the Alzheimer's Disease Neuroimaging Initiative ADNI consortium(Jack et al., 2008). (TR=2300 ms, TI=900 ms, flip angle=9°, BW240 Hz/pixel, FOV 256×240 mm, Matrix 256×256; resulting voxeldimensions: 1.0×1.0×1.1 mm, Acquisition time 9 min 14sec). Parallelimaging was used to improve the signal-to-noise ratio instead ofshortening the scan time — we obtained a single high-quality image

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instead of averaging two or more rapidly-acquired images. Imageswere inspected for motion artifact at the time of acquisition andscanning was repeated as necessary. The resultant images underwentnon-uniformity correction (Sled et al., 1998) and 3D-gradientunwarping (Jovicich et al., 2006) to correct for any geometric distor-tions arising from gradient non-linearity.

2D-FLAIR images obtained in the axial plane (TR=10,000 ms,TI=2500 ms, TE=96 ms, voxel dimensions 0.9×0.9×5.0 mm) wereused to evaluate for silent infarcts and tomeasure the volume of whitematter hyperintensities (not reported here). A neurologist reviewedimages showing potential pathological features or variants.

After the aforementioned exclusion criteria were met and imageswere evaluated for quality, 248 complete sets of demographic, health,MR imaging and neuropsychological data were subject to furtheranalysis.

MR image analysis

A standardizedMRI data-processingpipelinewasused to process thedata. Both manual and semi-automated measurements were made (forbrevity — the use of the semi-automatic methods will be referred to as‘automated measures’). Manual, interactive volumetry was performedfor Total Intracranial Volume (TIV), Hippocampus (HC), and ventriclevolume by two trained researchers using Analyze 7.0 (Mayo Clinic,Rochester MN) on graphic tablets (Wacom DTU-710, Wacom Saitama,Japan). The automated volume measurements were performed usingFreeSurfer 3.0.5 (http://surfer.nmr.mgh.harvard.edu/; Martinos Ima-ging Centre, Charlestown MA).

Manual measurements

Hippocampal volumes. The 3D T1-weighted sagittal images werefirst re-oriented in the coronal plane, orthogonal to the principal axisof the hippocampal (HC) formation. Images were enlarged by 4× andre-sampled using cubic spline interpolation. The landmarks used fortracing have been previously described (Jack et al., 1998,1989;Watsonet al., 1992) but see Supplementary Fig. 1 for some exemplars.Orthogonal views of the hippocampus were used to facilitate tracing.The first slice traced was one in which the crura of the fornices couldbe seen enface. Coronal images of the hippocampus were traced every2 mmmoving in the posterior–anterior direction. This resulted in 18–23 measured slices per person. The most anterior slice of thehippocampal head was determined retrospectively as the last sliceon which the hippocampus was visible. Brain sections were inspectedsequentially at 0.25 mm intervals until the hippocampus was nolonger visible. The volume of that eight-interval stack was scaledproportionally. Cavalieri's principle was used to compute volume.

Total intracranial volume (TIV). Total intracranial volume wasdetermined by tracing the margin of the inner table of the calvariumacross sagittal 3D T1-weighted images (Supplementary Fig. 2) andsummingup the volumes of sagittal slabs so obtained (Jack et al.,1989).Sectionswere traced every 6.6mm, starting from the right side, totaling17–22 measured slices per volunteer. The most lateral slice on whichcerebral cortical gyri were first visible was traced first. The inferior-most limit to tracing was the region across the foramen magnum.

Total ventricular volume. Total ventricular volume was obtained as asum of the volumes of two lateral, third and fourth ventricles(Supplementary Fig. 2). These were traced every 3 mm. Left andright lateral ventricles were measured simultaneously in the poster-ior–anterior direction, totaling 22–28 measured slices per participant.The slice in which the occipital horns of the lateral ventricles werevisible first was traced first. The slice in which the frontal horns of thelateral ventricles were visible was traced last. The third ventricle wastraced starting with the slice on which the anterior to the commis-

sure of the superior colliculus was visible first and ending with theslice posterior to the optic chiasm. Measurements of the fourthventricle were taken from every third slice in which this structurewas visible (approximately 5–7 slices per subject). Measurementbegan with the slice in which the inferior vermis was visible andended when the obex was seen. The cerebral aqueduct was includedin this measurement.

Inter-tracer reliability for manually traced volumes was evaluatedby comparing measurements of 10 randomly selected brains made bytwo tracers on two different occasions and separated by at least fourweeks. The intra-class correlation coefficient or ICC (Shrout andFleiss, 1979) were 0.93 for HC, 0.99 for TIV, and 0.99 for ventricles.

Automated measurements. Automated measurements of brainvolumes were performed using FreeSurfer 3.0.5 (http://surfer.nmr.mgh.harvard.edu/). Briefly, this involved the removal of non-braintissue using a hybrid watershed algorithm (Segonne et al., 2004), biasfield correction, automated Talairach transformation, segmentation ofsubcortical white matter and gray matter (including hippocampus,ventricles) (Fischl et al., 2002; Fischl et al., 2004), intensity normal-ization, tessellation of the gray/white matter boundary, automatedcorrection of topology defects, surface deformation to form the gray/white matter boundary and gray/cerebrospinal fluid boundary, andparcellation of cerebral cortex (including frontal cortex, parietalcortex, occipital cortex) (Desikan et al., 2006; Salat et al., 2004) basedon gyral and sulcal information derived from manually traced brains.Morphometric evaluation of each hemisphere was conducted inde-pendently. In the present work, we report total cerebral, total gray andtotal white matter volumes involving the cerebral hemispheres(excluding brain stem and cerebellum; see below) as well as selectedcortical structure gray matter volumes. All of these measures werecorrected for eTIV before statistical analysis.

Estimated total intracranial volume (eTIV). The eTIV was calculatedusing a validated method described elsewhere (Buckner et al., 2004).Briefly, an Atlas Scaling Factor (ASF) was determined based on thetransformation matrix of atlas normalization for each individualsubject. The ASF was then used to scale the TIV of the standardizedatlas brain to compute a given subject's TIV.

Head-size adjustment: All the volumetric measurements reportedhere showed significant gender differences before adjusting for head-size differences. The adjustment was performed on each volume ofinterest using the following analysis of covariance approach (Buckneret al., 2004; Mathalon et al., 1993):

Voladj = Volraw−b× TIV−TIV� �

where b is the slope of the linear regression between the brain volumeof interest and TIV . Note that this correction was not applied forcortical thickness measures.

Total cerebral volume (TCV). This was defined as the total volume ofcerebral gray matter, cerebral white matter, and subcortical structuresexcluding the cerebellar hemispheres. Constituent sub-volumes weresummed together before adjusting for head size differences.

Total ventricular volume. This was defined as the total volume oflateral ventricles, third ventricle and fourth ventricle. As ventricularmeasurements are positively skewed, we log-transformed the rawmeasurements before making adjustments for differences in head-size.

Hippocampal volume. The anatomical conventions employed byFreeSurfer for this measurement differ from those outlined for manualmeasurement in the current study. FreeSurfer estimates of hippocam-pal volumewere systematically higher than for manual measurement.Adjustments for head-size differences were performed separately for

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left, right and total hippocampal volumes. We reported these valuesfor comparison purposes but did not use them in our analyses.

Cerebral cortex gray matter and white matter volume. FreeSurfercomputes the volume of cortical gray matter using two methods — amodel based surface processing pipeline and a voxel based volumepipeline. The latter is an intensity-based method similar to that usedin voxel based morphometry packages like SPM. Here, we used theoutput of the surface pipeline, which modeled the cortical surfaceafter detecting the gray-white boundary and then ‘growing’ the pialsurface of gray matter. This yielded an estimate of the gray mattervolume of cerebral cortical surface (excluding subcortical nuclei likethe basal ganglia and thalamus) in addition to providing directmeasures of cortical thickness at each vertex. Cerebral white mattervolume was estimated by subtracting the volume of subcortical nucleiand ventricles from the contents of each cerebral hemisphereenclosed within the modeled gray matter mantle. These procedurescomply with recommendationsmade in the FreeSurferWiki at http://surfer.nmr.mgh.harvard.edu/fswiki.

Parcellated cortical structure volumes. The surface parcellationprocedure in FreeSurfer automatically assigns a neuroanatomicallabel to each gray matter voxel. This allows extraction of the graymatter volume for each cortical structure in a manner that has beenvalidated against expert manual tracing (Desikan et al., 2006). In thecurrent work, we selected a subset of FreeSurfer defined corticalregions based on prior structural and functional imaging data thatrelated structure to cognitive functions of interest (see Greenwood,2007; Raz, 2005; Reuter-Lorenz and Lustig, 2005 for recent reviews).These were inferior frontal, superior frontal, inferior parietal, superiorparietal, lateral occipital, lingual cortex, pericalcarine cortex, andfusiform cortex (Fig. 1) Parcellated volumes and cortical thicknessmeasures were corrected for eTIV.

Annualized percentage change (APC) values for the various brainvolumeswere estimated using themethod proposed by Raz (Raz et al.,2003b):

APC=Volb−VolaVola× b−að Þ×100

where b is the upper limit of the sample age rangewhile a is the lowerlimit of the sample age range. Vola and Volb are the predicted brain

Fig. 1. Brain surface parcellated into regions by FreeSurfer from data obtained from 236 volun(d) Superior parietal cortex; (e) Lateral occipital cortex; (f) Lingual cortex; (g) Pericalcarin

volumes at age and respectively using the regression equation fromthe cross-sectional regression of brain volumewith age. In the currentreport a=55 years and b=86 years.

Related data

In addition to the tests described above, each participant providedsociodemographic information (education, housing type), detailsconcerning substance use (cigarette and alcohol consumption),dietary history, exercise and leisure activity, as well as medicationand supplement intake. Weight, height and blood pressure weremeasured. Education was categorized into 5 classes: no formaleducation, 1–6 years, 7–9 years, 10–12 years and N12 years. Theseage bands represent points at which either scholastic ability or socio-economic factors resulted in a person having to leave school. Thesecond band represents primary education. The third representsschool leaving age for some of the older persons (a limitation of theeducation system at that time and locale). The fourth representscompletion of secondary education whereas the fifth band indicateseligibility for college or higher technical education. Over the last60 years, Singapore has transformed from a country when less than 1%had a college education to one where presently 25% are collegeeducated accounting for the range of education in this cohort.

A person was termed hypertensive if he/she had a systolic bloodpressure of ≥140 mm/Hg or a diastolic BP of ≥90 mm/Hg or was ontreatment for hypertension, irrespective of the blood pressuremeasurement taken for that day. A diabetic was defined as someonewith a fasting whole blood glucose level of ≥7.0 mmol/l or a personon treatment for diabetes mellitus.

Statistical analysis

Of the 349 respondents, 248 were deemed suitable for cross-sectional data analysis according to the inclusion criteria and imagingquality control measures previously outlined. Of the 101 participantsthat were excluded: Twenty (5.73%) either declined to undergo MRimaging or had images that were of insufficient quality, 4 (1.5%)showed pathological brain abnormalities on MR—we did not excludeindividuals with small basal ganglia infarcts that were asymptomatic;19 had significant health problems missed on initial screening; 17(4.87%) underwent coronary artery bypass surgery (patients with

teers. (a) Inferior frontal cortex; (b) Superior frontal cortex; (c) Inferior parietal cortex;e cortex; (h) Fusiform cortex.

Table 2Cognitive measures and their correlation with age

Cognitivedomains

Neuropsychological Test N Mean SD rage

Attention Digit span forward 248 10.6 2.51 −0.21⁎⁎(−0.19⁎⁎)Digit span backward 248 6.15 1.91

Spatial span forward 248 7.25 1.89Spatial span backward 248 6.31 2.12

Speed ofprocessing

Symbol digit modalitiestest (written)

248 42.5 10.3 −0.41⁎⁎⁎(−0.42⁎⁎⁎)

Symbol digit modalitiestest (oral)

248 49.4 10.6

Trail-making test A 248 45.1 19.3Verbalmemory

RAVLT −0.22⁎⁎(−0.20⁎⁎)Sums of trials 1–5 248 45.6 9.01

Immediate recall list A 247 9.77 2.88Delayed recall list A 248 9.88 3.02Recognition list A 246 13.4 2.02

Verbal paired associatesSums of trials 1–4 236 10.6 7.41Delayed recall 236 3.74 2.56Recognition 246 13.39 2.02

Visuospatialmemory

Visual reproduction −0.22⁎⁎(−0.19⁎⁎)Immediate recall 248 68.4 14.7

Delayed recall 248 44.3 18.2Visual paired associatesSums of trials 1–4 246 16.59 5.89Delayed recall 246 4.84 2.25

Executivefunction

Categorical fluency 248 42.82 8.64 −0.30⁎⁎⁎(−0.27⁎⁎⁎)Design fluency 248 21.58 7.62

Trail-making test B 230 119 97.6Language Object naming 247 71.1 7.19 −0.22⁎⁎

(−0.18⁎⁎)Action naming 247 40.6 5.85

Figures in parenthesis refer to correlation between age and cognitive performance aftercorrecting for gender and education.

⁎ pb .05.⁎⁎ pb .01.

⁎⁎⁎ pb .001.

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stents were excluded outright) and 2 (0.57%) had obstructive sleepapnoea; 35 (10%) had a MMSE score b26; 6 (1.7%) had a GeriatricDepression Screening Scale (GDS) score N9; and 24 were not righthanded (left-handed; n=12 (3.44%)), ambidexterous (n=12;(3.44%)).

Of the 248 eligible participants, 236 participants contributedcomplete brain imaging data as 12 participants had MR images thatdid not meet the stringent quality standards required for FreeSurferanalysis. This was a result of low contrast between gray and whitematter in the occipital region. The overall strategy for analysis was tostudy the correlates of age with cognition and brain measures,followed by other variables of interest with cognition and brainmeasures and finally the association between brain measures andperformance in 6 cognitive domains of interest. Partial correlationswere used to analyze the associations between other variables ofinterest and cognition or brain measures after factoring out con-founding covariates. The significance of these correlations was re-ported both prior to (pb .05) and after Bonferroni correction formultiple comparisons (adjusted threshold: pb .008). Multivariatelinear regression was applied to study the independent effect of ageon cognition after controlling for gender, education, BMI (body massindex=weight/(height)2), height and homocysteine. We appliedSteiger's Z⁎ statistic (Stieger, 1980) when determining whether slopesof cognitive decline (or volume decline) vs. age were significantlydifferent across cognitive measures (or brain measures) (Raz et al.,1997; Salat et al., 2004). Folate, homocysteine and vitamin B12 valueswere log transformed prior to further analysis. Data analysis wasperformed using SPSS version 16.0 (SPSS Inc, Chicago IL).

Results

Characteristics of the study population

The cohort was matched for age and gender (men: mean=65.9,SD=6.9 years and women: mean=65.6, SD=6.1 years; women52.8%; Table 1). 84% of participants had at least 10 years of education,substantially higher than that reported in a larger community-basedlongitudinal aging study conducted in the same city (Feng et al.,2006) but lower than that reported in most studies on Caucasian

Table 1Characteristics of the sample

n 248

Age, years 65.8 (6.53)Women, % 131 (52.8)Education, years 10.7 (3.46)BMI, kg/m2 23.4 (3.03)Systolic BP, mm Hg 132 (16.1)Diastolic BP, mm Hg 80.2 (9.23)Hypertension (all), % 57.3Fasting blood glucose, mmol/L 5.3 (1.0)Diabetes, % 12.5Total cholesterol, mmol/L 5.41 (0.89)LDL-C, mmol/L 3.30 (0.78)HDL-C, mmol/L 1.46 (0.36)Homocysteine, μmol/L 13.7 (4.36)Folate, nmol/L 25.9 (16.0)Vitamin B-12, pmol/L 431 (221)Ex-smoker 51 (20.6)Current smoker 8 (3.2)Do not consume alcohol 199 (80.2)APOE-ɛ4 heterozygotesa 46 (18.5)MMSE score 28.5 (1.16)GDS score 1.56 (1.76)

Values other than for gender are means (SD) or n (%). Abbreviations: BMI, Body-MassIndex; LDL, Low Density Lipoprotein; HDL, High Density Lipoprotein; APOE-ɛ4,Apolipoprotein Epsilon-4 allele.

a There were no APOE-ɛ4homozygotes in this cohort; MMSE, Mini-Mental StateExamination; GDS, Geriatric Depression Scale.

subjects. For comparison the national average in 1997 for residentnon-students N25 years of age was 8.8 for men and 7.1 years forwomen (http://www.singstat.gov.sg/stats/charts/lit-edu.html).Men were generally better educated (mean=11.4 years, SD=3.1)than women (mean=10.1 years, SD=3.6).

57.3% of volunteers were hypertensive of which 72.5% were ontreatment. 12.5% of volunteers had diabetes mellitus of which 94.5%were on treatment. 40.7% of all volunteers were not on anyprescription medication. There were few smokers (3.2%). The meanBMI of this cohort was 23.4 (SD=3.03). While low relative toCaucasian data, this figure is average in the East Asian context. Forsimilar levels of BMI, East Asians have been found to be at higher riskfor adverse cardiovascular outcomes (Deurenberg-Yap et al., 2000;Deurenberg and Deurenberg-Yap, 2003).

Effect of age on cognitive performance

Performance in all six cognitive domains declined with age(Table 2) and this effect remained significant after adjusting for theeffects of gender and education. The strongest correlation was seenbetween age and speed of processing (r=−0.41, pb .001), followedby executive function (r=−0.30, pb .001), visuospatial memory (r=−0.22, p=.003), language (r=−0.22, p=.003), attention (r=−0.21, p=.002) and verbal memory (r=−0.22, pb .002). Thecorrelation between speed of processing and age was significantlyhigher than for other cognitive domains and age (Steiger's Z⁎statistic=2.78, pb .05).

Effects of other variables on cognitive performance

Gender and education accounted for significant variance in cog-nitive performance over and above age (Table 3). Men showed

Table 3Effect of other variables on cognitive performance

Variable Attention Speed of processing Verbal memory Visuospatial memory Executive function Language

Gender: womena −0.20⁎⁎ −0.18⁎⁎ 0.25⁎⁎⁎ – – −0.20⁎⁎Educationb 0.25⁎⁎⁎ 0.55⁎⁎⁎ 0.29⁎⁎⁎ 0.33⁎⁎⁎ 0.43⁎⁎⁎ 0.47⁎⁎⁎BMIc, kg/m2 (−0.14⁎) −0.20⁎⁎ (−0.18⁎⁎) (−0.16⁎) – (−0.14⁎)Systolic BPc, mm Hg – – – – – –

Diastolic BPc, mm Hg – – – – – –

Fasting glucosec (mmol/L) – – – – – –

Total cholesterolc (mmol/L) – (0.15⁎) – – – –

Homocysteinec (μmol/L) – −0.20⁎⁎ – (−0.13⁎) – (−0.14⁎)Folatec (nmol/L) – (0.13⁎) – (0.13⁎) (0.14⁎) (0.13⁎)Vitamin B–12c (ρmol/L) – – – – – 0.17⁎⁎Heightc – – – 0.16⁎⁎ (0.13⁎) (0.12⁎)At least one e4 allele of APOEc – – – – – –

⁎pb .05; ⁎⁎pb .01; ⁎⁎⁎pb .001; If Bonferroni correction was used to account for multiple comparisons across the 6 cognitive variables, a corrected threshold of pb .008 was applied;correlations that did not meet this threshold appear parentheses.

a Gender was adjusted for age.b Education was adjusted for age and gender.c Other variables were adjusted for age, gender and education.

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superior performance in 3 out of 6 cognitive domains. They scoredhigher on attention (r=−0.20, p=.001), speed of processing (r=−0.18, p=.006) and language (r=−0.20, p=.002), while womenscored higher on verbal memory (r=0.25, pb .001). These gendereffects on attention (r=−0.16, p=.015) and verbal memory(r=0.31, pb .001) were significant even after adjusting for the effectsof age and education.

BMI continued to have a small but significant effect on speed ofprocessing even after correcting for age, gender, education andmultiple comparisons. In accordance with prior data from Caucasianpopulations (Schulz, 2007) as well as a prior study from the same city(Feng et al., 2006), higher homocysteine levels were negativelycorrelated with cognitive performance. Height, a proxy for early lifebrain development (Abbott et al., 1998; Beeri et al., 2005) waspositively correlated with visuospatial memory. APOE ɛ4 status,systolic blood pressure and fasting blood glucose did not indepen-dently correlate with cognitive scores in this analysis. However, itshould be noted that the ranges of blood pressure and glucose wererestricted in this relatively healthy population.

Multivariate linear regression showed comparable findings for theeffects of age on cognition after controlling for confounders — gender,education, BMI, height and homocysteine (Supplementary Table 1).

Comparison of manual and automated volumetric measurements

Automated measurements of TIV (r=0.87; pb .001), total brainvolume (r=0.98; pb .001) and ventricles (r=0.98; pb .001) werevery highly correlated with manual measurements (Table 4). Thecorrelation between manual and automated measures of thehippocampus was lower (r=0.82; pb .001) as expected given the

Table 4Comparison of manual and automatic volumetric measurements

Variables Unadjusted

Manual Automated r

Total intracranial volume 1462.7 (130.6) 1399.1 (139.6) 0.87⁎⁎Total brain volumea 1009.4 (135.6) 1175.5 (137.2) 0.98⁎⁎Hippocampus 6.56 (0.72) 7.60 (0.83) 0.82⁎⁎Left hippocampus 3.22 (0.37) 3.66 (0.41) 0.79⁎⁎Right hippocampus 3.34 (0.37) 3.94 (0.44) 0.79⁎⁎

Ventricular volumeb 1.33 (0.18) 1.40 (0.17) 0.98⁎⁎

Volumes are mean values (SD) in cm3.a TBV, n=18 (includes cerebellum but excludes ventricles); for other measures

n=236.b Correlations involving ventricular volumes were computed using log-transformed

measures.⁎⁎ all correlations were significant at pb .001.

small size of this structure and the different landmarks used forsegmenting this structure. As a result, further analyses utilized onlymanually measured hippocampal volumes.

Effects of age on brain measures

Men had larger heads than women as reflected by higher intra-cranial volume (Mean:men 1548 cm3 vs. women 1386 cm3; difference11%; pb .001) but the effect of gender across all brain measures wasnegated after correcting for TIV/eTIV. This was in keeping with recentreports using higher quality brain imaging and measurementtechniques (Buckner et al., 2004; Scahill et al., 2003).

After correcting for head size, which negated the influence ofgender and height on these measures, total cerebral volume and totalhippocampal volume showed significant age-related decline (Fig. 2).The correlation between age and total cerebral volume (r=−0.46;pb .001) was higher than the correlation between age and hippo-campal volume (−0.37; pb .001). This might be expected from thegreater variability arising from measuring a small complex structurelike the hippocampus. Both total cerebral and total hippocampalvolumes declined at approximately 0.45%/year, with wider 95%confidence intervals for the latter. Ventricles enlarged at a rate ofaround 4.9%/yr (Table 5).

Cortical surface gray matter volume declined at an estimated0.33%/yr; 95% CI (−0.40 to −0.14%) and cerebral white mattervolume contracted at a comparable rate of 0.41%/yr; 95% CI (− .60 to−0.25%); Table 6, Fig. 2. We found comparable regional rates ofdecline of graymatter volume across several cortical ROI in the frontal,parietal and occipital lobes (Table 6; Fig. 3). Apart from the lingualgyri, no other brain region showed a comparably robust rate of declinewith age relative to total cerebral volume.

Effects of other variables on brain measurements

After correcting for head size, only plasma homocysteine showedany correlation with brain measures. Plasma homocysteine showed anegative correlation with white matter volume (r=−0.25, pb .001)and a positive correlationwith ventricular volumes (r=0.19, pb .005).There were no significant correlations between BMI, blood pressure,fasting blood glucose or total cholesterol and manually obtained brainmeasures. After accounting for age, only the effect of homocysteine oncerebral white matter volume (r=−0.18, pb .01) remained signifi-cant. In contrast to its strong effects on cognitive performance,education was not correlated with any brain measure, excepting amodest correlation with cortical thickness in the left inferior frontalregion.

Fig. 2. Scatter plots depicting the effect of age on brain measures using automated (n=236) and manual (n=248) measurements. Ventricular volumes were log-transformed.

Table 5MRI imaging volume data: correlations between age and brain measures

ROI Volume (cm3)adjusted

rage Annual percentagechange (95% CI)

Total cerebral volume 873.54 (50.58) −0.46⁎⁎ −0.40 (−0.57 to −0.27)Hippocampus 6.52 (0.65) −0.37⁎⁎ −0.54 (−0.87 to −0.30)Right hippocampus 3.32 (0.33) −0.36⁎⁎ −0.51 (−0.86 to −0.28)Left hippocampus 3.20 (0.33) −0.36⁎⁎ −0.53 (−0.89 to −0.29)

Ventricular volumea 1.32 (0.18) 0.45⁎⁎ 4.85 (2.87–6.73)

Volumes were adjusted for total intracranial volume (TIV or eTIV as appropriate).a Ventricular volumes were log-transformed prior to computing correlation.

⁎⁎ all correlations were significant at pb .001.

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Correlations between brain measurements and cognitive performance

As older participants in this cohort had smaller heads (representedby TIV or eTIV; decline in TIV of 0.23%/yr) and since TIV or eTIV wereused to normalize brain measurements, it was important to evaluatehow this finding relates to cognitive performance. After accounting forage, we found no deleterious effects of smaller head size on any of thecognitive domains evaluated.

Therewere positive correlations between total cerebral volume andspeed of processing (r=0.28, pb .001), visuospatial memory (r=0.21,pb .001), executive function (r=0.19, pb .01) and attention (r=0.17,pb .01; Table 7). Total ventricular volume correlated negatively withspeed of processing (r=−0.24, pb .001), executive function (r=−0.22, pb .001) verbal memory (r=−0.14, pb .05) and visuospatialmemory (r=−0.14, pb .05). The latter two correlations did notsurvive correction for multiple comparisons. These correlations wereidentical for both manual and automated measurements of totalcerebral volume. Manually measured left hippocampal volumesshowed weak positive correlations with visuospatial memory and

executive function that did not survive correction for multiplecomparisons and which were not replicated in the correspondingautomated measures.

Within specific cortical regions of interest, speed of processingshowed significant positive correlation with gray matter volume inbilateral inferior frontal (R: r=0.24, L: r=0.18, both pb .01; Fig. 4) and

Table 6Correlations between age and additional automatically determined brain measures

ROI Brodmann area Adjusted volume(cm3)

rage Annual percentagechange (95% CI)

Cerebral grey matterRight – 199.77 (10.6) −0.32⁎⁎ −0.26 (−0.41 to −0.14)Left 197.52 (10.1) −0.33⁎⁎ −0.26 (−0.40 to −0.14)

Cerebral white matterRight – 239.18 (14.3) −0.41⁎⁎ −0.40 (−0.60 to −0.25)Left 239.34 (14.2) −0.40⁎⁎ −0.40 (−0.60 to −0.25)

Inferior frontal gyrusRight 44,45,47 8.81 (1.1) −0.17⁎ −0.33 (−0.80 to −0.05)Left 8.67 (1.1) – –

Superior frontal gyrusRight 8, 9 18.07 (1.7) −0.17⁎ −0.26 (−0.54 to −0.04)Left 19.41 (1.8) −0.22⁎⁎ −0.30 (−0.46 to −0.10)

Inferior parietal cortexRight 19,39 11.89 (1.3) −0.16⁎ −0.21 (−0.66 to −0.04)Left 10.03 (1.2) −0.17⁎⁎ −0.32 (−0.79 to −0.06)

Superior parietal cortexRight 7 10.76 (1.2) −0.17⁎ −0.30 (−0.71 to −0.06)Left 10.79 (1.3) −0.17⁎⁎ −0.33 (−0.82 to −0.07)

Lateral occipital cortexRight 17,18,19 10.53 (1.5) −0.21⁎⁎ −0.46 (−1.09 to −0.14)Left 10.58 (1.4) – –

LingualRight 17,18 5.75 (0.8) −0.33⁎⁎ −0.69 (−1.34 to −0.32)Left 5.13 (0.8) −0.21⁎⁎ −0.53 (−1.36 to −0.13)

Pericalcarine cortexRight 17 2.10 (0.3) −0.15⁎ −0.38 (−1.18 to −0.03)Left 1.66 (0.3) – –

Fusiform gyrusRight 37 6.91 (1.1) – –

Left 7.05 (1.2) −0.15⁎ −0.39 (−1.2 to −0.04)

⁎pb .05; ⁎⁎pb0.01.

Table 7Correlations between MRI brain measures and cognitive performance

Variable Attention Speed ofprocessing

Verbalmemory

Visuospatialmemory

Executivefunction

Language

Total cerebralvolume

0.17⁎⁎ 0.28⁎⁎⁎ – 0.21⁎⁎⁎ 0.19⁎⁎ –

Hippocampus – – – – (0.14⁎) –

Righthippocampus

– – – – – –

Lefthippocampus

– – – (0.13⁎) (0.16⁎) –

Ventricularvolumea

– −0.24⁎⁎⁎ (−0.14⁎) (−0.14⁎) −0.22⁎⁎⁎ –

⁎pb .05; ⁎⁎pb .01; ⁎⁎⁎pb .001. If Bonferroni correction was used to account for multiplecomparisons across the 6 cognitive variables a corrected threshold of pb .008 wasapplied; correlations that did not meet this threshold appear parentheses.

a Ventricular volumes were log-transformed prior to computing correlation.

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superior parietal regions (R: r=0.18, pb .01) as well as the lingualgyrus (R: r=0.21. pb .001, Table 8). Notably, in the automatedparcellation scheme used here, the lingual gyrus is adjacent to theinferior lip of the pericalcarine cortex that was referred to as‘pericalcarine cortex’ (Raz et al., 2005) and ‘primary visual cortex’(Salat et al., 2004) in prior studies. Left superior frontal gyrus cortical

Fig. 3. Surface maps of age-related cortical thinning (blue) obtained after controlling for eTIV

volume showed significant positive correlations with attention, speedof processing and visuospatial memory.

When the effects of age were controlled for, most of the significantcorrelations disappeared except for those relating speed of processingto both inferior frontal gyri (L: r=0.17, R: r=0.19, both pb .01) as wellas attention in relation to the right lingual gyrus (r= 0.15, pb .05),indicating that age accounted for most of the observed variance.

Discussion

The present cross-sectional study is the first sizable combinedMRI imaging, neuropsychological and health variable study per-formed on a cohort of healthy aged volunteers arising from a single,East Asian ethnic group. The study cohort is unique in that mostparticipants were born and grew up in a developing country butaged in a developed one.

We found speed of processing to be the most age-affected cog-nitive domain. It was associated with commensurate decline in totalcerebral hemisphere volume.Whitematter volume loss was at least asprominent as gray matter decline. Regionally, there was relativelygreater volume loss in the lateral prefrontal cortex bilaterally, around

. On the inflated brain, dark gray regions represent gyri and lighter areas represent sulci.

Fig. 4. Surface maps showing cortical areas in which there was significant correlation between cortical thickness and speed-of-processing scores (after controlling for eTIV).On theinflated brain, dark gray regions represent gyri and lighter areas represent sulci.

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the primary visual cortex as well as bilateral superior parietal cortices.Contrary to popular expectation, despite differences in diet, lifestyle,body structure and a lower frequency of APOE e4 carriers in our EastAsian cohort, the pattern of change in cognition and brain measureswas broadly comparable to similar studies conducted in Caucasianpopulations and speaks to the generalizability of processes involved inage-related decline in cognition and brain volume.

Table 8Correlations between regional brain volumes and cognitive performance

Variable Attention Speed ofprocessing

Verbalmemory

Visuospatialmemory

Executivefunction

Language

Cerebral grey matterLeft – 0.17⁎⁎ – 0.18⁎⁎ (0.16⁎) –

Right – (0.14⁎) – (0.16⁎) (0.13⁎) –

Cerebral white matterLeft (0.17⁎) 0.27⁎⁎ 0.23⁎⁎ 0.19⁎⁎ 0.29⁎⁎ (0.15⁎)Right (0.15⁎) 0.26⁎⁎ 0.22⁎⁎ 0.20⁎⁎ 0.28⁎⁎ (0.15⁎)

Inferior frontal gyrusLeft – 0.18⁎⁎ – – – –

Right (0.15⁎) 0.24⁎⁎ – – (0.14⁎) –

Superior frontal gyrusLeft (0.15⁎) (0.15⁎) – (0.14⁎) – –

Right – – – – – –

Superior parietal cortexLeft – (0.13⁎) (0.15⁎) – – –

Right – 0.18⁎⁎ – (0.14⁎) (0.14⁎) –

LingualLeft – (0.15⁎) – – – (0.13⁎)Right 0.20⁎⁎ 0.21⁎⁎ – 0.18⁎⁎ (0.14⁎) –

Fusiform gyrusLeft – – – (0.13⁎) – –

Right – – – – – –

⁎pb .05; ⁎⁎pb .01. There were no significant correlations between cognition and brainmeasures in the inferior parietal cortex, lateral occipital cortex and pericalcarine cortex.If Bonferroni correction was used to account for multiple comparisons across the 6cognitive variables a corrected threshold of pb .008 was applied; correlations that didnot meet this threshold appear parentheses.

Decline in cognition with age and effects of other variables

We found that age affected speed of processing more severelythan other cognitive domains. Education exerts considerable influ-ence on cognitive performance (Staff et al., 2004) and, in this cohort,it had a large effect on speed of processing and executive function,contributing 30% and 18.5% of the variance in these cognitive domainsrespectively. The elderly in the present study had 3–5 years lessformal education compared to volunteers in prior imaging studies(Fotenos et al., 2005; Raz et al., 2005). Despite this difference, most ofthe structural imaging findings we observed were quite similar tothose reported in Caucasian populations.

Consistent with several cross-sectional (Duthie et al., 2002; Eliaset al., 2005; Feng et al., 2006) and prospective (Kado et al., 2005; Nurket al., 2005) studies on aged individuals, we found elevated homo-cysteine levels to be associated with poorer cognitive performance,serving to generalize these findings to a population with differentdietary habits. More specifically, elevated levels of homocysteinewerelinked to psychomotor slowing (Prins et al., 2002; Schafer et al., 2005)and poorer episodic visual memory (Elias et al., 2005). Plasmaconcentrations of folate were weakly associated with speed of pro-cessing, executive functions and episodic visual memory (de Lau etal., 2007; Feng et al., 2006; Ramos et al., 2005). While homocysteineand folate levels were correlated (r=0.4, pb0.01), they appear toexert dissociable effects on the brain as evidenced by their differentialeffects on brain measures.

Age-related brain atrophy: independent of education or cohort effectson head size

The negative correlation between intracranial volume and ageobserved here has not been reported in studies conducted indeveloped countries, when only elderly volunteers were analyzed(Edlandet al., 2002; Jenkins et al., 2000; Lemaitre et al., 2005; Raz et al.,2005) possibly reflecting the poorer early-life socio-economic condi-tions and nutrition in the current cohort.

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Intracranial size has been suggested as a surrogate marker of‘cognitive reserve’ (MacLullich et al., 2002; Schofield et al., 1997), butseveral studies have found no correlation between head size and riskof dementia (Edland et al., 2002; Jenkins et al., 2000). Here, we foundno deleterious association between intracranial size and cognitivescores apart from attention. Although men had larger heads thanwomen, the effects of gender on brain measures were not significantafter correcting for head size, as in previous studies (Buckner et al.,2004; Lemaitre et al., 2005).

Age-related changes in brain measures

Total brain volume is the most extensively reported measure inbrain aging research and is associated with an annual percent change(APC) of 0.18–0.88%/yr with an average around 0.5%/yr in the agegroup we tested (Jack et al., 2005; Preboske et al., 2006; Raz et al.,2007). Another well-studied metric is hippocampal volume; APC 0.3–1.5%/yr depending on age (Fox and Schott, 2004; Jack et al., 2005). Ourcross-sectional estimates concerning both measures (total cerebralvolume APC 0.4%/yr; hippocampal APC 0.5%/yr) are at the low endrelative to studies that evaluated or analyzed only elderly subjects(Fotenos et al., 2005; Raz et al., 2005) but are higher than reports thatevaluated volume change from young adulthood to senescence(Jernigan and Gamst, 2005). In addition to rate of decline, thevariance of brainmeasures andwhether they increase in the oldest old(Scahill et al., 2003) or not (Fotenos et al., 2005) is important toconsider. Our relatively healthy cohort did not show increasedvariance of brain measures with age (see scatter plots in Fig. 2).

White matter volume declined at an equivalent rate as graymatter volume in the present cohort. This is in keeping with othernewer studies involving elderly volunteers (Fotenos et al., 2005;Ikram et al., 2008) as well as some studies evaluating volumes acrossa large age span (Guttmann et al., 1998). Since white matter volumepeaks as late as the fourth decade of life (Bartzokis et al., 2003),studies that evaluate age effects on white matter volume across thelife span may yield smaller estimates of white matter decline orshow no significant changes (Pfefferbaum et al., 1994). We did notfind more precipitous decline with increasing age as suggested bysome (Guttmann et al., 1998) although this might be a result ofhaving few very old (N85 years) participants.

Age-related change in white matter volume in both hemispheres(Table 8) roughly paralleled the corresponding declines in domainspecific performance (Table 2) in keeping with the notion that whitematter changes play an important role in age-related cognitive decline(Bucur et al., 2008; Walhovd and Fjell, 2007).

Like others, we found regional differences in age-related brainatrophy (DeCarli et al., 2005; Jernigan et al., 2001; Lemaitre et al.,2005; Raz et al., 1997, 2005; Resnick et al., 2003; Salat et al., 2004).There is uniform agreement that age-related decline of frontal lobevolume occurs primarily in the lateral prefrontal (Raz et al., 2005;Salat et al., 2004) and/or orbito-frontal cortex (Lemaitre et al., 2005;Raz, 2005; Resnick et al., 2003). The present study concurred, andadditionally identified significant age-related decline in lateralprefrontal cortex volume.

There is less agreement concerning regional atrophy elsewhere inhealthy elderly volunteers. After the frontal lobe, some studies havereported lateral temporal atrophy (DeCarli et al., 2005; Jernigan et al.,2001) whereas others have emphasized shrinkage of the parietallobes (Lemaitre et al., 2005; Resnick et al., 2003). There is strongdisagreement regarding the occipital lobe around the primary visualcortex where cortical thinning has been reported as being prominent(Lemaitre et al., 2005; Salat et al., 2004) or insignificant (Raz et al.,1997, 2005). The cortical mantle in this region is very thin and it ispossible that older MR image data may not contain sufficient reso-lution tomake the distinctions that newer systems can (the importantpoint is that the point spread function of the imaging data is the

appropriate measure of revealed anatomical detail and not ‘resolution’as measured by the density of the imaging matrix).

Using a similar methodology to Salat, we reproduced the findingthat there is age-related thinning around the primary visual cortexand a striking absence of significant changes in the lateral temporalneocortex (Salat et al., 2004). This finding serves to remind thatbefore evaluating the significance of regional changes in brainvolume with age, the critical reader should take into account thelack of common analysis methodology across studies (but see(Kennedy et al., 2008)) as well as the heterochronicity of age-relatedregional changes in cortical thickness (Salat et al., 2004; Sowell etal., 2003). When considering the findings of the present study, itshould be kept in mind that the age range used was restricted tosubjects from 55–85 years and unlike life-span studies on aging, willnecessarily show smaller correlations between age and structuralbrain measures.

Effects of other variables on brain measures

While there was a clear effect of education on cognitive per-formance, particularly in speed of processing, education did notinfluence age-related decline of total cerebral volume (adjusted foreTIV). Education explained some of the variance associated withcortical thickness in the left inferior frontal region, a region that alsoshowed correlations with speed of processing. This finding contrastswith reports suggesting that non-demented elderly individuals withbetter education have a higher ‘brain reserve’ and may remaincognitively intact despite harboring greater brain atrophy (Coffeyet al., 1999; Fotenos et al., 2008). The dissociation between overallbrain volume and the cognitive benefit of education suggests that thelatter may primarily operate at the level of synaptic function, orimproved neuronal connectivity rather than increasing neural bulk ina regionally specific fashion as suggested by studies involving specificcognitive abilities like navigation, juggling or musical talent (Dra-ganski and May, 2008).

Of the vascular risk factors, higher blood pressure (Goldstein et al.,2002; Heijer et al., 2003; Wiseman et al., 2004), elevated homo-cysteine (den Heijer et al., 2003; Sachdev et al., 2004), BMI (Gustafsonet al., 2004; Ward et al., 2005) and diabetes (van Harten et al., 2006)have been associated with greater brain atrophy. However, the brainmeasurement techniques in these patient based studies are crudecompared to those applied to the evaluation of healthy cognitiveaging, MCI or AD.

Here, we found that although elevated homocysteine wasassociated with cerebral white matter atrophy and ventricularvolume, this effect was not pronounced enough to consistently affecttotal cerebral volume. Prior studies have shown that whereas there isstrong agreement that elevated homocysteine negatively affectscognition there is less agreement as to whether this is mediatedthrough brain atrophy (den Heijer et al., 2003) or white matterhyperintensities (Sachdev et al., 2004). Although elevated BMI had anegative effect on cognition, we did not find correlations betweenBMI and brain volumes. There were no significant correlationsbetween blood pressure or blood sugar on brain volumes. However,this could be a result of range restriction of these values in thishealthy cohort.

Total cerebral measures may suffice in assessing cognition–brainstructure relationships in healthy subjects

We found that adjusted total cerebral volume was the brainmeasure that showed the highest correlation with variables thataffected cognition as well as with age-related change in cognitiveperformance. Correlations of this metric with speed of processing,executive function, visuospatial memory and attention were alwayspositive, in keeping with the ‘bigger is better’ relationship between

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whole brain volume and cognition (Posthuma et al., 2002; Staff et al.,2006; Walhovd et al., 2005).

In contrast, although hippocampal volume parallels memorydecline in Alzheimer's disease (Jack et al., 1998), this correlationdoes not extend to healthy, non-demented volunteers evaluatedusing memory measures commonplace in clinical practice (Rodrigueand Raz, 2004; Van Petten, 2004). Only in the context of specializedtesting, such as when long-term memory was tested 11 weeks afterencoding has hippocampal volume in normal elderly been correlatedwith memory performance (Walhovd et al., 2004). It should benoted that the anatomical structures supporting such memories inhealthy individuals could also include neocortical regions (Walhovdet al., 2006).

The advent of automated cortical segmentation that has beenvalidated across scanners provides a potentially important advance inenabling the correlation of cognition and regional cortical thickness(Dickerson et al., 2008). However, the results of the present studysuggest that while regional differences in correlations betweendifferent cognitive domains exist, the effects are small and may notbe larger than the effects found using whole brain, total gray or whitematter volumes. This said, it remains possible that as in the case ofthe hippocampus and memory, or executive function and the frontallobes (Van Petten et al., 2004) the dissociation between more specificstructural–cognition relationships when comparing normal subjectsand patients with lesions could reflect the insensitivity of neuropsy-chological tests designed for clinical use.

Summary

The broad agreement between age-related changes in cognitionand brain measures reported here compared to studies based onCaucasian populations argues for the presence of common factorsthat modulate brain aging across ethnic groups that potentially differin culture, diet and lifestyle. Total cerebral measures appear toprovide adequate brain–cognition correlations with performance onclinical neuropsychological tests in healthy elderly. However, toevaluate the structural neural correlates of variables that modulatecognition in this population, more sensitive neuropsychological testsor measures of structural integrity like diffusion tensor imaging maybe helpful.

Disclosure

The authors have no conflict of interests to disclose. All authorshave reviewed the contents of the manuscript being submitted,approve of its contents and validate the accuracy of the data.

Acknowledgments

Arne Littmann provided proprietary homogeneity correction andgradient distortion correction techniques. Jenni Pacheco provided on-site training for the use of FreeSurfer. Cliff Jack provided valuableadvice onmanual morphometry and the quality control aspects of thisstudy. This work was supported by the Biomedical Research Council,Singapore: BMRC 04/1/36/372 and A⁎STAR: SRP R-913-200-004-304.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.neuroimage.2009.01.036.

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